Scholarly Works (4 results)

Inspired by the Open mHealth application architecture, which emphasizes user-controlled data security, reusable common modules and inter-operability among different mHealth applications, this dissertation introduces NDNFit, a mobile health (mHealth) application built on the Named Data Networking (NDN) architecture, while offer users the familiar user experience as traditional mHealth applications. An equally important motivation for NDNFit is NDN's application-driven architecture development philosophy -- NDNFit serves as a use case to experiment with integrating multiple NDN components into one coherent application ecosystem, as well as drive the design and development of NDN architecture and protocol.

During the design and implementation process of NDNFit, we identified and solved five problems. First, because NDN mandates that all data packets be authenticated, NDNFit builds a namespace and certificate management system to manage identity and certificate, and defines trust policies for consumers to verify data packets; to protect confidentiality, it employs NAC to encrypt Data content, and makes the first effort to obscure Data names. Second, NDNFit employs Named Function Networking (NFN) to implement data processing services, defines name conversion services to enable sharing of named functions across multiple mHealth applications, and designs key delegation mechanisms for named functions to properly secure processed data. Third, to provide reliable data storage service, NDNFit designs DSU command protocol for users to communication with the storage, and designs mechanisms to employ State Vector Synchronization (SVS) protocol to replicate data in a distributed data storage system. Fourth, NDNFit introduces catalog and denial of existence packets for efficient data transferring among different components. Last, NDNFit recognizes the issue of application mobility and refine forwarding hint to enable data reachability and support producer application mobility.

The NDNFit design illustrates that NDN's data-centric approach to networking -- naming and securing data directly, and sharing namespace between application layer and network layer -- provides a superior solution over the existing TCP/IP based solution for Open mHealth application architecture, as NDN network primitives enable users' control over their own data and facilitate interoperability among multiple applications, without relying on underlying transport layers and other third party services. The NDNFit design also demonstrates the power of NDN naming conventions -- how Data packets are named, and what is the relationship between different Data names -- in simplifying application design, that good naming conventions can help in many aspects: enable data-centric security, speed up data dissemination, or even improve data reachability.

At the end of this dissertation, we present the initial implementation of NDNFit as well as its demonstration on NDN testbed. Experimental results show that NDNFit design works well, and the lessons we learned from NDNFit design and development can benefit those of future NDN-based applications.

Electron tomography in materials science has flourished with the demand to characterize nanoscale materials in three dimensions (3D). Access to experimental data is vital for developing and validating reconstruction methods that improve resolution and reduce radiation dose requirements. This work presents five high-quality scanning transmission electron microscope (STEM) tomography datasets in order to address the critical need for open access data in this field. The datasets represent the current limits of experimental technique, are of high quality, and contain materials with structural complexity. Included are tomographic series of a hyperbranched Co2P nanocrystal, platinum nanoparticles on a carbon nanofibre imaged over the complete 180° tilt range, a platinum nanoparticle and a tungsten needle both imaged at atomic resolution by equal slope tomography, and a through-focal tilt series of PtCu nanoparticles. A volumetric reconstruction from every dataset is provided for comparison and development of post-processing and visualization techniques. Researchers interested in creating novel data processing and reconstruction algorithms will now have access to state of the art experimental test data.

Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis.

Height is a classic complex trait with common variants in a growing list of genes known to contribute to the phenotype. Using a genecentric genotyping array targeted toward cardiovascular-related loci, comprising 49,320 SNPs across approximately 2000 loci, we evaluated the association of common and uncommon SNPs with adult height in 114,223 individuals from 47 studies and six ethnicities. A total of 64 loci contained a SNP associated with height at array-wide significance (p < 2.4 × 10(-6)), with 42 loci surpassing the conventional genome-wide significance threshold (p < 5 × 10(-8)). Common variants with minor allele frequencies greater than 5% were observed to be associated with height in 37 previously reported loci. In individuals of European ancestry, uncommon SNPs in IL11 and SMAD3, which would not be genotyped with the use of standard genome-wide genotyping arrays, were strongly associated with height (p < 3 × 10(-11)). Conditional analysis within associated regions revealed five additional variants associated with height independent of lead SNPs within the locus, suggesting allelic heterogeneity. Although underpowered to replicate findings from individuals of European ancestry, the direction of effect of associated variants was largely consistent in African American, South Asian, and Hispanic populations. Overall, we show that dense coverage of genes for uncommon SNPs, coupled with large-scale meta-analysis, can successfully identify additional variants associated with a common complex trait.